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Aggregative and stochastic model of main path identification: a case study on graphene

Author

Listed:
  • Woondong Yeo

    (Korea University
    Korea Institute of Science and Technology Information)

  • Seonho Kim

    (Korea Institute of Science and Technology Information)

  • Jae-Min Lee

    (Korea Institute of Science and Technology Information)

  • Jaewoo Kang

    (Korea University)

Abstract

This paper suggests a new method to search main path, as a knowledge trajectory, in the citation network. To enhance the performance and remedy the problems suggested by other researchers for main path analysis (Hummon and Doreian, Social Networks 11(1): 39–63, 1989), we applied two techniques, the aggregative approach and the stochastic approach. The first technique is used to offer improvement of link count methods, such as SPC, SPLC, SPNP, and NPPC, which have a potential problem of making a mistaken picture since they calculate link weights based on a individual topology of a citation link; the other technique, the second-order Markov chains, is used for path dependent search to improve the Hummon and Doreian’s priority first search method. The case study on graphene that tested the performance of our new method showed promising results, assuring us that our new method can be an improved alternative of main path analysis. Our method’s beneficial effects are summed up in eight aspects: (1) path dependent search, (2) basic research search rather than applied research, (3) path merge and split, (4) multiple main paths, (5) backward search for knowledge origin identification, (6) robustness for indiscriminately selected citations, (7) availability in an acyclic network, (8) completely automated search.

Suggested Citation

  • Woondong Yeo & Seonho Kim & Jae-Min Lee & Jaewoo Kang, 2014. "Aggregative and stochastic model of main path identification: a case study on graphene," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 633-655, January.
    • Handle: RePEc:spr:scient:v:98:y:2014:i:1:d:10.1007_s11192-013-1140-3
    DOI: 10.1007/s11192-013-1140-3
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    References listed on IDEAS

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    1. John S. Liu & Louis Y.Y. Lu, 2012. "An integrated approach for main path analysis: Development of the Hirsch index as an example," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    2. Howard D. White, 2001. "Authors as citers over time," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(2), pages 87-108.
    3. John S. Liu & Louis Y.Y. Lu, 2012. "An integrated approach for main path analysis: Development of the Hirsch index as an example," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    4. Bart Verspagen, 2007. "Mapping Technological Trajectories As Patent Citation Networks: A Study On The History Of Fuel Cell Research," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 93-115.
    5. Bhupatiraju, Samyukta & Nomaler, Önder & Triulzi, Giorgio & Verspagen, Bart, 2012. "Knowledge flows – Analyzing the core literature of innovation, entrepreneurship and science and technology studies," Research Policy, Elsevier, vol. 41(7), pages 1205-1218.
    6. Francis Narin & Gabriel Pinski & Helen Hofer Gee, 1976. "Structure of the Biomedical Literature," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 27(1), pages 25-45, January.
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    Citations

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    Cited by:

    1. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    2. Kim, Erin H.J. & Jeong, Yoo Kyung & Kim, YongHwan & Song, Min, 2022. "Exploring scientific trajectories of a large-scale dataset using topic-integrated path extraction," Journal of Informetrics, Elsevier, vol. 16(1).
    3. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    4. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Xu, Haiyun & Yang, Guancan, 2022. "A semantic main path analysis method to identify multiple developmental trajectories," Journal of Informetrics, Elsevier, vol. 16(2).
    5. Lu, Louis Y.Y. & Liu, John S., 2016. "A novel approach to identify the major research themes and development trajectory: The case of patenting research," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 71-82.
    6. Krzysztof Klincewicz, 2016. "The emergent dynamics of a technological research topic: the case of graphene," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 319-345, January.
    7. John S. Liu & Louis Y. Y. Lu & Mei Hsiu-Ching Ho, 2019. "A few notes on main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 379-391, April.
    8. Bruno Miranda Henrique & Vinicius Amorim Sobreiro & Herbert Kimura, 2018. "Building direct citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 817-832, May.
    9. Kuan, Chung-Huei, 2024. "Integrating prior field knowledge as key documents with main path analysis utilizing key-node path search," Journal of Informetrics, Elsevier, vol. 18(3).

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    More about this item

    Keywords

    Main path analysis; Second-order Markov chains; Markov model; Historiography; Quantitative method;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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